Atmospheric levels of ammonia (NH.sub.3) have substantially increased during the last century, posing a hazard to both human health and environmental quality. The atmospheric budget of NH.sub.3, however, is still highly uncertain due to an overall lack of observations. Satellite observations of atmospheric NH.sub.3 may help us in the current observational and knowledge gaps. Recent observations of the Cross-track Infrared Sounder (CrIS) provide us with daily, global distributions of NH.sub.3 . In this study, the CrIS NH.sub.3 product is assimilated into the LOTOS-EUROS chemistry transport model using two different methods aimed at improving the modeled spatiotemporal NH.sub.3 distributions. In the first method NH.sub.3 surface concentrations from CrIS are used to fit spatially varying NH.sub.3 emission time factors to redistribute model input NH.sub.3 emissions over the year. The second method uses the CrIS NH.sub.3 profile to adjust the NH.sub.3 emissions using a local ensemble transform Kalman filter (LETKF) in a top-down approach. The two methods are tested separately and combined, focusing on a region in western Europe (Germany, Belgium and the Netherlands). In this region, the mean CrIS NH.sub.3 total columns were up to a factor 2 higher than the simulated NH.sub.3 columns between 2014 and 2018, which, after assimilating the CrIS NH.sub.3 columns using the LETKF algorithm, led to an increase in the total NH.sub.3 emissions of up to approximately 30 %. Our results illustrate that CrIS NH.sub.3 observations can be used successfully to estimate spatially variable NH.sub.3 time factors and improve NH.sub.3 emission distributions temporally, especially in spring (March to May). Moreover, the use of the CrIS-based NH.sub.3 time factors resulted in an improved comparison with the onset and duration of the NH.sub.3 spring peak observed at observation sites at hourly resolution in the Netherlands. Assimilation of the CrIS NH.sub.3 columns with the LETKF algorithm is mainly advantageous for improving the spatial concentration distribution of the modeled NH.sub.3 fields. Compared to in situ observations, a combination of both methods led to the most significant improvements in modeled monthly NH.sub.3 surface concentration and NH4+ wet deposition fields, illustrating the usefulness of the CrIS NH.sub.3 products to improve the temporal representativity of the model and better constrain the budget in agricultural areas.